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Assimilation of SLA along track observations in the Mediterranean with an oceanographic model forced by atmospheric pressure
Language
English
Obiettivo Specifico
3.7. Dinamica del clima e dell'oceano
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Title of the book
Issue/vol(year)
/8(2012)
ISSN
1812-0784
Electronic ISSN
1812-0792
Pages (printed)
787-795
Issued date
2012
Keywords
Abstract
A large number of SLA observations at a high
along track horizontal resolution are an important ingredient
of the data assimilation in the Mediterranean Forecasting
System (MFS). Recently, new higher-frequency SLA products
have become available, and the atmospheric pressure
forcing has been implemented in the numerical model used
in the MFS data assimilation system. In a set of numerical experiments,
we show that, in order to obtain the most accurate
analyses, the ocean model should include the atmospheric
pressure forcing and the observations should contain the atmospheric
pressure signal. When the model is not forced
by the atmospheric pressure, the high-frequency filtering of
SLA observations, however, improves the quality of the SLA
analyses. It is further shown by comparing the power density
spectra of the model fields and observations that the model
is able to extract the correct information from noisy observations
even without their filtering during the pre-processing.
along track horizontal resolution are an important ingredient
of the data assimilation in the Mediterranean Forecasting
System (MFS). Recently, new higher-frequency SLA products
have become available, and the atmospheric pressure
forcing has been implemented in the numerical model used
in the MFS data assimilation system. In a set of numerical experiments,
we show that, in order to obtain the most accurate
analyses, the ocean model should include the atmospheric
pressure forcing and the observations should contain the atmospheric
pressure signal. When the model is not forced
by the atmospheric pressure, the high-frequency filtering of
SLA observations, however, improves the quality of the SLA
analyses. It is further shown by comparing the power density
spectra of the model fields and observations that the model
is able to extract the correct information from noisy observations
even without their filtering during the pre-processing.
Type
article
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